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Technologies in the language services industry - LAN00115M

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  • Department: Language and Linguistic Science
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2024-25

Module summary

This core module will introduce you to the theory and functional principles of the latest technologies being used and developed in the language services industry (for example, Artificial Intelligence). You will use group projects and problem-based learning to apply acquired knowledge to real-world scenarios. Throughout the module, you will learn key technological principles which will allow you 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 2024-25

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 develop your ability to self-learn any relevant technologies emerging in the future, on the basis of functional principles you have learned as part of the module. You will acquire theoretical knowledge during lectures, and gain expert guidance in practical sessions. Group study is an integral 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 learned during 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
Groupwork 70

Special assessment rules

None

Indicative reassessment

Task % of module mark
Essay/coursework 100

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 summative assessment.
  • 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-centered design approach. CRC Press.

Carl, M., & Braun, S. (2017). Translation, interpreting and new technologies. In The Routledge handbook of translation studies and linguistics. (pp. 374-390). Routledge.

Domingos, P. (2012). A few useful things to know about machine learning. Communications of the 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 for predictive 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 language processing: 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 in artificial intelligence and computational linguistics (Vol. 119). Springer Science & Business Media.

Patel, R., & Patel, S. (2021). Deep learning for natural language processing. In Information and Communication Technology for Competitive Strategies (ICTCS 2020) Intelligent Strategies for ICT (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 Intelligence Research, 69, 343-418.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.