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Module Code - Title:


Year Last Offered:


Hours Per Week:













Grading Type:


Prerequisite Modules:

Rationale and Purpose of the Module:

Today's translation technology includes a range of different practical applications from computer-assisted translation technology and machine translation to quality assurance and terminology management tools. This module prepares students for the implementation and use of such applications. It outlines the fundamental principles of natural language processing as well as major types of translation technology. It also identifies challenges that arise in the development and implementation of translation technology. Thus, it provides students with the skills and insights to evaluate translation technology and implementation strategies to address the technology's limitations.


The potential of language engineering to solve particular problems. The main components in a language engineering system. The main approaches to key tasks such as sentence alignment or named entity recognition. Translation technology in use, rationale for using it and its challenges and opportunities (Translation memory, Translator workbench, Terminology systems, Alignment technology, Machine translation - MT etc.). Principal issues and best practices in multilingual digital content management. The complexity of solving linguistic problems using computational methods. Approaches to localisation engineering and testing. Variants of localisation engineering and testing approaches. Different approaches to MT. The contribution which MT can make, as compared to the use of translators. Realistic application scenarios for MT.

Learning Outcomes:

Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

On successful completion of this module, students should be able to: 1. Explain prevalent translation technology tools and their underpinning approaches. 2. Differentiate between variant approaches. 3. Evaluate different approaches for specific scenarios. 4. Indentify complexities in the underpinning approaches. 5. Predict limitations in application scenarios.

Affective (Attitudes and Values)

On successful completion of this module, students should be able to: 1 - Acknowledge limitations in current translation technology. 2 - Differentiate between different types of tools and underlying technology. 3 - Initiate improvements in implementation and use for specific scenarios.

Psychomotor (Physical Skills)


How the Module will be Taught and what will be the Learning Experiences of the Students:

Students will review the literature on translation technology and underpinning approaches through journal papers, industry white papers and case studies (knowledgeable). Students will be made to give group based (collaborative) presentations (articulate) in which they identify and discuss the latest trends in translation technology (proactive), as well as their potential applications to the workplace (creative), based on a literature review.

Research Findings Incorporated in to the Syllabus (If Relevant):

Prime Texts:

Jurafsky D. (2009) Speech and Language processing: An introduction to NLP, Computational linguistics and speech recognition , Prentice Hall

Other Relevant Texts:

Bird, S.; Klein, E.; Loper, E. (2009) Natural Language Processing with Python , O'Reilly
Koehn, P. (2009) Statistical Machine Translation , Cambridge University Press
Alakrota, A.; Murray, L.; Nikolov, N. (2018) Dataset Construction for the Detection of Anti-Social Behaviour in Online Communication in Arabic , The 4th International Conference on Arabic Computational Linguistics (ACLing 2018), November 17-19 2018, Dubai, United Arab Emirates

Programme(s) in which this Module is Offered:

Semester - Year to be First Offered:

Module Leader: