SMART TECHNOLOGIES FOR TRANSLATION - 2022/3
Module code: TRAM502
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice in time for the start of the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
The module explores the main theoretical and practical aspects of smart technologies for translation, with emphasis on how to use methods from Natural Language Processing and Corpus Linguistics to help translators. The purpose of this module is to enable students to understand the challenges faced when using computers to process text automatically or when they need to process speech as an input. The module will provide students with knowledge about the fields of Machine Translation (MT), Natural Language Processing (NLP), and Corpus Linguistics (CL).
The module will start with an introduction to NLP and machine translation and will present different paradigms to produce automatic translations. Students will be provided with hands-on experience on how to train translation engines, and how it is possible to evaluate MT. Other topics such as terminology extraction, quality estimation, speech recognition and translation and data acquisition will also be covered. The students will learn how to harvest relevant corpora from the web, clean them and use them for translation-related tasks.
Students will learn how they can use existing tools to achieve various tasks related to smart technologies for translation. Knowledge of programming will not be necessary, but students who have a programming background will be given the opportunity to use this knowledge in the module.
School of Literature and Languages
ORASAN Constantin (Lit & Langs)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: 101130
Module cap (Maximum number of students): N/A
Overall student workload
Workshop Hours: 11
Independent Learning Hours: 128
Captured Content: 11
Prerequisites / Co-requisites
Indicative module content:
• Introduction to Natural Language Processing (NLP) and Machine Translation (MT)
• 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
|Assessment type||Unit of assessment||Weighting|
|Coursework||An essay on a given topic (1,000-1,200 words)||40|
|Practical based assessment||Portfolio containing solutions to exercises given during the semester||60|
The assessment strategy is designed to provide students with the opportunity to demonstrate:
- Knowledge and understanding of the approaches used in the area of smart technologies for translation.
- Knowledge of how to use existing tools and develop simple programs to help them achieve their tasks.
Thus, the summative assessment for this module consists of:
- Portfolio of solutions to the weekly homework. 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.
- An essay on a given topic (1,000 - 1,200 words). 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.
- Formative assessment will focus on student participation and class discussions throughout the module.
- The students will be able to discuss their solutions to the homework before submitting the revised portfolio.
- Students will be provided with detailed written feedback following coursework assignments.
- Verbal feedback will also occur in class and individual appointments if required.
- Provide a thorough overview of the basic concepts involved in using smart technologies for translation
- Give students the ability to understand the strengths and weaknesses of automatic processing tools in translation workflows, including hybrid workflows which include speech recognition
- Provide students with a sound understanding of the relevant methodologies
- Enable students to select suitable NLP, CL and MT tools for their needs
- Give students hands on experience with using relevant tools and writing simple programs to process their data
|001||Demonstrate an in-depth knowledge base of specific topics within the area of smart technologies for translation||CK|
|002||Demonstrate practical skills in using a wide variety of state-of-the-art tools and resources relevant to NLP, CL, and MT||CKP|
|003||Demonstrate a critical understanding of the published literature and current debates in these areas||CKT|
|004||Demonstrate ability to communicate findings in writing||PT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
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.
The learning and teaching methods include:
• Lectures and workshops will be interspersed with opportunities for group and whole class discussions (22 hours)
• Contact hours will be complemented with materials for and activities for guided study posted on SurreyLearn (8 hours)
• Self-study (120 hours)
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: TRAM502
Programmes this module appears in
|Translation MA||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Translation and Interpreting MA||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
|Translation and Interpreting Studies MRes||2||Optional||A weighted aggregate mark of 50% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.