TRAM502 Smart Technologies for Translation

By the end of the module students will be able to:  

• Demonstrate an in-depth knowledge base of specific topics within the area of smart technologies for translation 
• Demonstrate practical skills in using a wide variety of state-of-the-art tools and resources relevant to NLP, CL, and MT 
• Demonstrate a critical understanding of the published literature and current debates in these areas 
• Demonstrate ability to communicate findings in writing 
• Appreciate new societal, technological and language-industry demands 

• Introduction to Natural Language Processing (NLP) and Machine Translation (MT)  
• Using Large Language Models like ChatGPT to solve translation related problems  
• Existing paradigms in MT, how to train an MT engine and how to evaluate MT  
• Building corpora from the Web using data scraping and cleaning of files  
• Building parallel corpora and automatic alignment of corpora  
• Terminology extraction  

The learning and teaching strategy is designed to provide students with a good understanding of the practical aspects of using NLP and CL in translation workflows towards a smarter use of technologies in translation. This is in line with the MA in Translation’s overall aims of enhancing students’ background in technologies for translation. 

Learning and teaching includes:  

• Workshops with opportunities for group and whole-class discussions 
• Captured content addressing module content 
• Guided learning – such as signposted hands-on exercises and guidelines relevant to advanced practice in the field 
• Problem-based practical exercises  
• Discussion and group work (in-class) 
• Practice-based learning – application of knowledge acquired throughout the module in realistic or academically simulated contexts 

An Essay on a Given Topic (1,000 - 1,200 words) (40%) 

Students will have to submit an essay on one of the more theoretical topics covered in the first half of the semester. The essay will be due at the middle of the semester. 

Portfolio of Solutions to the Weekly Homework (60%)

 
Students will be given practical homework every two weeks and will be asked to prepare a portfolio with their answers. In some cases, students will be asked to write "small essays" (250-300 words) discussing a practical topic, whilst in other cases they will need to explain their experience using a smart technology. The portfolio can be seen as diary of the practical activities covered in this module and it is expected to indicate any problems that the students encountered and how they solved them. Part of the workshops will be used to discuss the solution for the homework given in the first half of the module. After these discussions, students will be asked to update their portfolio with reflective analysis of their initial solutions. All the pieces of homework given during the semester will have to be included in the final portfolio, which will be due at the end of the semester. The marking will focus both on the answers to the homework and on the reflective analysis. 

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