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2021/2022

Localisation and AT

Code: 43776 ECTS Credits: 15
Degree Type Year Semester
4315970 Tradumatics: Translation Technologies OB 0 1
The proposed teaching and assessment methodology that appear in the guide may be subject to changes as a result of the restrictions to face-to-face class attendance imposed by the health authorities.

Contact

Name:
Olga Torres Hostench
Email:
Olga.Torres.Hostench@uab.cat

Use of Languages

Principal working language:
catalan (cat)

Other comments on languages

See in Contents the language for each subject.

Teachers

Tianqi Zhang
Gokhan Dogru
Eduardo Simon Jimenez
Manuel Mata Pastor
Carme Mangiron Hevia
María Pilar Sánchez Gijón
Olga Torres Hostench
Oscar Nogueras Bastardo

External teachers

Anna Civil
Felipe Sánchez Martínez

Prerequisites

Having taken, or taking, the previous MA modules.

Objectives and Contextualisation

  • Learn the principles of localization.
  • Learn the principles of machine translation.
  • Learn the principles of localization engineering.
  • Learn how to use translation management and editing systems for localization and machine translation.
  • Learn to use translation management and editing systems for the localization of websites, software and apps.
  • Learn the specificities of video game localization.
  • Learn about the different types of automatic translation and the associated profiles and processes.

Competences

  • Analyse the structure of digital products based on markup languages and their overall coherence for translation.
  • Continue the learning process, to a large extent autonomously.
  • Define, evaluate and solve problems related to translation technologies.
  • Integrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.
  • Know the professional translation and post-editing market: its profiles, requirements and socio-economic role.
  • Make efficient use of assisted translation and correction software.
  • Make informed, well-reasoned decisions in the field of translation technologies.
  • Manage one's own knowledge consistently and systematically, in coordination with other persons and independently, with the emphasis on quality.
  • Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.

Learning Outcomes

  1. Continue the learning process, to a large extent autonomously.
  2. Create and manage localisation databases.
  3. Define strategies for translating projects.
  4. Define the fundamental principles of localisation.
  5. Detect the implicit intertexts in the product.
  6. Identify the code and the translatable text in digital products.
  7. Identify the problems associated with machine translation and define strategies for machine translation of high quality.
  8. Identify the problems associated with the translation of digital products and offer solutions in terms of both localisation and basic programming.
  9. Integrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.
  10. Integrate machine translation into assisted translation software.
  11. Make informed, well-reasoned decisions in the field of translation technologies.
  12. Manage one's own knowledge consistently and systematically, in coordination with other persons and independently, with the emphasis on quality.
  13. Provide a translation of digital products that meets the requirements of the client and the translation situation.
  14. Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.

Content

This module focuses on localization in its many variants (web, software, apps and video games) and machine translation (MT) and its variants (TAE, TABR, TAN and TAPE). 

Localization

  • Principles of Localization and MT (Machine Translation). Teacher: Olga Torres Hostench (Catalan).
  • memoQ: translation management and editing systems for localization and MT. Teacher: Gokhan Dogru  (English).
  • DVX (Déjà Vu X): translation management and editing systems for localization and MT. Teacher: Tianqi Zhang  (Catalan).
  • OmegaT: translation management and editing systems for localization and MT. Teacher: Gokhan Dogru (English).
  • memsource: translation management and editing systems for localization and MT. Teacher: Gokhan Dogru (English).
  • SEO (Search Engine Optimisation): How do SEO techniques improve the localization of a project? How to translate taking into account SEO criteria? Teacher: Oscar Nogueras (Spanish)
  • Localization engineering: technical processes for extracting translatable texts from localization formats. Teacher: Eduard Simon (Catalan).
  • Localization of apps: description of the localization of apps for mobile devices in the iOS and Android operating systems. Teacher: Eduard Simon (English).
  • Localization case studies seminar: description of professional localization processes, from the reception of a project to its delivery. Teacher: Manuel Mata (Spanish).
  • Localization of videogames: description of the localization of videogames and their specificities. Teacher: Carme Mangiron (Catalan).

Machine translation

  • SMT (Statistical Machine Translation). Teachers: Felipe Sánchez (Spanish).
  • RBMT (Rules-Based AutomaticTranslation). Teacher: Anna Civil (Catalan).
  • MTPE (Machine Translation and Post-editing). Teacher: Pilar Sánchez (Spanish).

Methodology

  • Theoretical lectures
  • Seminars
  • Task-based classes for solving problems / cases / exercises
  • Practical exercises in the classroom
  • Reading: books / articles / reports
  • Self-study
  • Writing reports / coursework

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Activities

Title Hours ECTS Learning Outcomes
Type: Directed      
Training activities carried out in the classroom 94 3.76 2, 4, 3, 5, 12, 6, 7, 8, 10, 13, 11, 9, 14, 1
Type: Supervised      
Training activities supervised by the teaching staff 47 1.88 2, 4, 3, 5, 12, 6, 7, 8, 10, 13, 11, 9, 14, 1
Type: Autonomous      
Training activities carried out by the student on a self-study basis outside the classroom. 234 9.36 2, 4, 3, 5, 12, 6, 7, 8, 10, 13, 11, 9, 14, 1

Assessment

  • 10% Active participation
  • 20% Submitting report on translation tools. Teacher: Gokhan Dogru/Tianqi Zhang
  • 5% Mastering practical knowledge about Machine Translation (TAE). Teacher: Felipe Sánchez
  • 5% Mastering practical knowledge about Machine Translation (TBR). Teacher: Anna Civil
  • 10% Mastering practical knowledge on localization of videogames. Teacher: Carme Mangiron
  • 10% Mastering practical knowledge about SEO. Teacher: Óscar Nogueras
  • 10% Mastering practical knowledge on localization engineering. Teacher: Eduard Simon
  • 10% Mastering practical knowledge on localization of apps. Teacher: Eduard Simon
  • 10% Mastering practical knowledge on localization cases. Teacher: Manuel Mata
  • 10% Mastering practical knowledge on machine translation post-editing. Teacher: Pilar Sánchez Gijón

 

Related matters

 

The above information on assessment, assessment activities and their weighting is merely a guide. The subject's lecturer will provide full information when teaching begins.

 

Review

 

When publishing final marks prior to recording them on students' transcripts, the lecturer will provide written notification of a date and time for reviewing assessment activities. Students must arrange reviews in agreement with the lecturer.

 

Missed/failed assessment activities

 

Students may retake assessment activities they have failed or compensate for any they have missed, provided that those they have actually performed account for a minimum of 66.6% (two thirds) of the subject's final mark and that they have a weighted average mark of at least 3.5. Under no circumstances may an assessment activity worth 100% of the final mark be retaken or compensated for.

 

The lecturer will inform students of the procedure involved, in writing, when publishing final marks prior to recording them on transcripts. The lecturermay set one assignment per failed or missed assessment activity or a single assignment to cover a number of such activities.

 

Classification as "not assessable"

 

In the event of the assessment activities a student has performed accounting for just 25% or less of the subject's final mark, their work will be classified as "not assessable" on their transcript.

 

Misconduct in assessment activities

 

Students who engage in misconduct (plagiarism, copying, personation, etc.) in an assessment activity will receive a mark of “0” for the activity in question. In the case of misconduct in more than one assessment activity, the students involved will be given a final mark of “0” for the subject.

 

Students may not retake assessment activities in which they are found to have engaged in misconduct. Plagiarism is considered to mean presenting all or part of an author's work, whether published in print or in digital format, as one's own, i.e. without citing it. Copying is considered to mean reproducing all or a substantial part of another student's work. In cases of copying in which it is impossible to determine which of two students has copied the work of the other, both will be penalised.

Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Active participation 10% 0 0 2, 4, 3, 5, 12, 6, 7, 8, 10, 13, 11, 9, 14, 1
Control of practical knowledge 70% 0 0 2, 4, 3, 5, 12, 6, 7, 8, 10, 13, 11, 9, 14, 1
Submission of reports and assignments 20% 0 0 2, 4, 3, 5, 12, 6, 7, 8, 10, 13, 11, 9, 14, 1

Bibliography

The teacher of each content will provide the corresponding bibliography.

 

Diaz Fouces, O., García González, M. (eds.) (2008). Traducir (con) software libre. Granada: Comares.

Esselink, B. (2000). A practical guide to localization. Amsterdam/Philadelphia: John Benjamins.

Jiménez-Crespo, M. A. (2013). Translation and Web Localization. Milton Park, Abingdon, Oxon: Routledge.

Kenny, D. (2009). Corpora. En: Mona Baker y Gabriela Saldanha (eds.), Routledge encyclopedia of translation studies (p. 59-62). Londres: Routledge.

Martín-Mor, A.; Piqué, R.; Sánchez-Gijón, P. (2016). Tradumàtica: Tecnologies de la traducció. Vic: Eumo Editorial.

O’Hagan, M. (2009). "Computer-aided translation (CAT)". En: Mona Baker y Gabriela Saldanha (eds.), Routledge encyclopedia of translation studies (p. 48-51). Londres: Routledge.

Oliver, A. (2016). Herramientas tecnológicas para traductores. Barcelona: UOC.

Oliver, A.; Moré, Q. (2007). Les tecnologies de la traducció. Barcelona: UOC.

Ping, K. (2009). "Machine translation". En: Mona Baker y Gabriela Saldanha (eds.), Routledge encyclopedia of translation studies (p. 162-168). Londres: Routledge.

Somers, H. (ed.) (2003). Computers and translation: A translator’s guide. Amsterdam-Philadelphia: John Benjamins.

Software

Assisted-translation tools

Localizacion engineering tools

Apps localization tools

Videogame localization tools

Localization tools

Machine translation tools

Machine translation post-editing tools

Free software and commercial software