On Education and Training in Translation Quality Assessment

  • Stephen DohertyEmail author
  • Joss Moorkens
  • Federico Gaspari
  • Sheila Castilho
Part of the Machine Translation: Technologies and Applications book series (MATRA, volume 1)


In this chapter, we argue that education and training in translation quality assessment (TQA)is being neglected for most, if not all, stakeholders of the translation process, from translators, post-editors, and reviewers to buyers and end-users of translation products and services. Within academia, there is a lack of education and training opportunities to equip translation students, even at postgraduate level, with the knowledge and skills required to understand and use TQA. This has immediate effects on their employability and long-term effects on professional practice. In discussing and building upon previous initiatives to tackle this issue, we provide a range of viewpoints and resources for the provision of such opportunities in collaborative and independent contexts across all modes and academic settings, focusing not just on TQA and machine translation training, but also on the use of assessment strategies in educational contexts that are directly relevant to those used in industry. In closing, we reiterate our argument for the importance of education and training in TQA, on the basis of all the contributions and perspectives presented in the volume.


Translation quality assessment Principles to practice Translation industry Translation students Translation teaching Translation pedagogy 



This work has been partly supported by the ADAPT Centre for Digital Content Technology which is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.


  1. Beeby A, Fernández M, Fox O, Hurtado Albir A, Kozlova I, Kuznik A, Neunzig W, Rodríguez-Inés P, Romero L, Wimmer S, Hurtado Albir A (2009) Results of the validation of the PACTE translation competence model: acceptability and decision making. Across Lang Cult 10(2):207–230CrossRefGoogle Scholar
  2. Bowker L, Marshman E (2010) Towards a model of active and situated learning in the teaching of computer-aided translation: introducing the CERTT project. J Trans Stud 13(1/2):199–226Google Scholar
  3. Chan S-W (ed) (2010) Journal of translation studies special issue: The teaching of computer-aided translation 13(1&2). The Chinese University of Hong Kong and The Chinese University Press, Hong KongGoogle Scholar
  4. Christensen TP, Schjoldager A (2016) Computer-aided translation tools: the uptake and use by Danish translation service providers. JoSTrans 25:89–105Google Scholar
  5. Delizée A (2011) A global rating scale for the summative assessment of pragmatic translation at Master’s level: an attempt to combine academic and professional criteria. In: Depraetere I (ed) Perspectives on translation quality. Walter de Gruyter, Berlin, pp 9–24Google Scholar
  6. Depraetere I, Vackier T (2011) Comparing formal translation evaluation and meaning-oriented translation evaluation: or how QA tools can(not) help. In: Depraetere I (ed) Perspectives on translation quality. Walter de Gruyter, Berlin, pp 25–50CrossRefGoogle Scholar
  7. Doherty S (2016) The impact of translation technologies on the process and product of translation. Int J Commun 10:947–969Google Scholar
  8. Doherty S (2017) Issues in human and automatic translation quality assessment. In: Kenny D (ed) Human issues in translation technology. Routledge, London, pp 131–148Google Scholar
  9. Doherty S, Kenny D (2014) The design and evaluation of a statistical machine translation syllabus for translation students. Interpret TransTrain 8(2):295–315Google Scholar
  10. Doherty S, Moorkens J (2013) Investigating the experience of translation technology labs: pedagogical implications. JoSTrans 19:22–136Google Scholar
  11. Doherty S, Kenny D, Way A (2012) Taking statistical machine translation to the student translator. In: Proceedings of the tenth conference of the Association for Machine Translation in the Americas, San Diego.
  12. EAMT/BCS (2002) Proceedings of the BCS/EAMT workshop on Teaching Machine Translation. Organised by the European Association for Machine Translation in association with the British Computer Society Natural Language Translation Specialist Group. UMIST, Manchester, England, 14–15 November 2002. Available via: Accessed 12 May 2017
  13. EMT Expert Group (2009) Competences for professional translators, experts in multilingual and multimedia communication. European Master’s in Translation (EMT). Available via: Accessed 5 Jan 2018
  14. EMT Expert Group (2017) European Master’s in Translation Competence Framework 2017. European Master’s in Translation (EMT). Available via: Accessed 9 Feb 2018
  15. Englard M (1958) The end of translators? Linguist Rev 1958(1):26–27Google Scholar
  16. Federico M, Cattelan A, Trombetti M (2012) Measuring user productivity in machine translation enhanced computer assisted translation. In: Proceedings of the tenth biennial conference of the Association for Machine Translation in the Americas (AMTA), San Diego, October 28–November 1 2012Google Scholar
  17. Flanagan M, Christensen TP (2014) Testing post-editing guidelines: how translation trainees interpret them and how to tailor them for translator training purposes. Interpret Trans Train 8(2):257–275CrossRefGoogle Scholar
  18. Forcada M (2003) A 45-hour computers in translation course. In: Proceedings of Machine Translation Summit IX, New Orleans, USA, 23–27 September 2003, no page numbersGoogle Scholar
  19. Forcada ML, Pérez-Ortiz JA, Lewis DR (2001) MT Summit VIII workshop on teaching Machine Translation. Santiago de Compostela. Available via: Accessed 12 May 2017
  20. García I (2011) Translating by post-editing: is it the way forward? Mach Transl 25(3):217–238CrossRefGoogle Scholar
  21. Gaspari F, Hutchins J (2007) Online and free! Ten years of online machine translation: origins, developments, current use and future prospects. In: Proceedings of Machine Translation Summit XI, Copenhagen, 10–14 September 2007, pp 199–206Google Scholar
  22. Gaspari F, Almaghout H, Doherty S (2015) A survey of machine translation competences: insights for translation technology educators and practitioners. Perspect Stud Translatol 23(3):333–358CrossRefGoogle Scholar
  23. Granell Zafra J (2006) The adoption of computer-aided translation tools by freelance translators in the UK. Dissertation, Loughborough UniversityGoogle Scholar
  24. Higher Education Academy (2012) A marked improvement: transforming assessment in higher education. Higher Education Academy. Available via: Accessed 22 Mar 2018
  25. Huertas Barros E, Vine J (2017) Current trends on MA translation courses in the UK: changing assessment practices on core translation modules. Interpret Trans Train 12(1):5–24CrossRefGoogle Scholar
  26. Karamanis N, Luz S, Doherty G (2011) Translation practice in the workplace: contextual analysis and implications for machine translation. Mach Transl 25(1):35–52CrossRefGoogle Scholar
  27. Kenny D (2007) Translation memories and parallel corpora: challenges for the translation trainer. In: Kenny D, Ryou K (eds) Across boundaries: international perspectives on translation. Cambridge Scholars Publishing, Newcastle-upon-Tyne, pp 192–208Google Scholar
  28. Kenny D, Doherty S (2014) Statistical machine translation in the translation curriculum: overcoming obstacles and empowering translators. Interpret Trans Train 8(2):276–294CrossRefGoogle Scholar
  29. Kenny D, Way A (2001) Teaching machine translation and translation technology: a contrastive study. In: Proceedings of MT Summit VIII workshop on teaching translation, Santiago de Compostela, Spain, 18 September 2001, pp 13–17Google Scholar
  30. Kingscott G (1990) Session 4: summary of the discussion. In: Proceedings of translating and the Computer 10: the translation environment 10 years on. 10–11 November 1988, London, pp 161–164Google Scholar
  31. Knight K (2003) Teaching statistical machine translation. In: Proceedings of Machine Translation Summit IX, New Orleans, USA, 23–27 September 2003, no page numbersGoogle Scholar
  32. Koo SL, Kinds H (2000) A quality-assurance model for language projects. In: Sprung RC (ed) Translating into success: cutting-edge strategies for going multilingual in a global age. John Benjamins, Amsterdam, pp 147–157CrossRefGoogle Scholar
  33. Koponen M (2015) How to teach machine translation post-editing? Experiences from a post-editing course. In: Proceedings of the 4th workshop on post-editing technology and practice, Miami, USA, 3 November, pp 2–15Google Scholar
  34. Marshman E, Bowker L (2012) Translation technologies as seen through the eyes of educators and students: harmonizing views with the help of a centralized teaching and learning resource. In: Hubscher-Davidson S, Borodo M (eds) Global trends in translator and interpreter training. Bloomsbury, London, pp 69–95Google Scholar
  35. Mitamura T, Nyberg E, Frederking R (2003) Teaching machine translation in a graduate language technologies program. In: Proceedings of Machine Translation Summit IX, New Orleans, USA, 23–27 September 2003, no page numbersGoogle Scholar
  36. Moorkens J (2017) Under pressure: translation in times of austerity. Perspect Stud Trans Theory Pract 25(3):464–477Google Scholar
  37. Moran J, Lewis D, Saam C (2018) Can user activity data in CAT tools help us measure and improve translator productivity? In: Corpas Pastor G, Durán-Muñoz I (eds) Trends in E-tools and resources for translators and interpreters. Brill, Leiden, pp 137–152Google Scholar
  38. O’Brien S (2012) Translation as human-computer interaction. Transl Spaces 1:101–122CrossRefGoogle Scholar
  39. O’Hagan M (2013) The impact of new technologies on translation studies: a technological turn? In: Millán C, Bartrina F (eds) The Routledge handbook of translation studies. Routledge, Abingdon, pp 503–518Google Scholar
  40. Pym A (2003) Redefining translation competence in an electronic age: in defence of a minimalist approach. Meta 48(4):481–497CrossRefGoogle Scholar
  41. Pym A (2013) Translation skill-sets in a machine-translation age. Meta 58(3):487–503MathSciNetCrossRefGoogle Scholar
  42. Robichaud B, L’Homme M-C (2003) Teaching the automation of the translation process to future translators. In: Proceedings of Machine Translation Summit IX, New Orleans, USA, 23–27 September 2003, no page numbersGoogle Scholar
  43. Scarpa F, Orlando D (2017) What it takes to do it right: an integrative EMT-based model for legal translation competence. JoSTrans 27:21–42Google Scholar
  44. Sycz-Opoń J, Gałuskina K (2017) Machine translation in the hands of trainee translators: an empirical study. Stud Log Gramm Rhetor 49(1):195–212Google Scholar
  45. Taillefer L (1992) The history of the relationship between machine translation and the translator. In: Proceedings of the 33rd annual conference of the American Translators Association. Learned Information, Medford, pp 161–165Google Scholar
  46. Vertan C, von Hahn W (2003) Specification and evaluation of machine translation toy systems: criteria for laboratory assignments. In: Proceedings of Machine Translation Summit IX, New Orleans, USA, 23–27 September 2003, no page numbersGoogle Scholar
  47. Wältermann D (1994) Machine translation systems in a translation curriculum. In: Dollerup C, Lindegaard A (eds) Teaching translation and interpreting 2: insights, aims, visions. John Benjamins, Amsterdam, pp 309–317CrossRefGoogle Scholar
  48. Way A, Gough N (2003) Teaching and assessing empirical approaches to machine translation. In: Proceedings of Machine Translation Summit IX, New Orleans, USA, 23–27 September 2003, no page numbersGoogle Scholar
  49. Way A, Hearne M (2011) On the role of translations in state-of-the-art statistical machine translation. Lang Linguist Compass 5(5):227–248CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Stephen Doherty
    • 1
    Email author
  • Joss Moorkens
    • 2
  • Federico Gaspari
    • 3
    • 4
  • Sheila Castilho
    • 3
  1. 1.School of Humanities and Languages, The University of New South WalesSydneyAustralia
  2. 2.ADAPT Centre/School of Applied Language and Intercultural StudiesDublin City UniversityDublinIreland
  3. 3.ADAPT Centre/School of ComputingDublin City UniversityDublinIreland
  4. 4.University for Foreigners “Dante Alighieri” of Reggio CalabriaReggio CalabriaItaly

Personalised recommendations