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Multilingual Systems, Translation Technology and Their Impact on the Translator’s Profession

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Abstract

Starting from an overview of multilingual systems, I point out the usefulness of machine translation in some translation contexts, especially in dynamic environments. Then I describe how electronic tools can be useful for the human translator and how machine translation can be integrated into translation memory systems in the translation workflow. After this general panorama, I enumerate a series of studies in the field of translation studies that deal with translation process research and investigate the interaction between human translators and those technologies. I note that the main aspects being investigated are productivity, quality and effort. I also mention how the new technologies might affect the translation market and the activity of translation professionals. I conclude by indicating some areas for future research, including tool usability and job satisfaction.

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Notes

  1. 1.

    In the context of computer software, a “locale” can be defined as “a collection of standard settings, rules and data specific to a language and geographical region” (Esselink 2000, 471).

  2. 2.

    A “bot” is a virtual contact that will reply to each of your chat messages by translating it into a chosen language. For more details, see http://support.google.com/talk/bin/answer.py?hl=en-US&answer=172257&topic=1190&ctx=topic.

  3. 3.

    http://research.google.com/about.html.

  4. 4.

    http://research.microsoft.com/apps/dp/areas.aspx?a=47189.

  5. 5.

    http://www.research.ibm.com/compsci/spotlight/nlp/.

  6. 6.

    http://www.statmt.org/moses.

  7. 7.

    http://www.letsmt.eu.

  8. 8.

    http://www.euromatrixplus.net.

  9. 9.

    http://project.cgm.unive.it/stilven_en.html.

  10. 10.

    http://www.pluto-patenttranslation.eu.

  11. 11.

    http://www.apertium.org.

  12. 12.

    While MT research in the United States tends to be done within (big) companies, in Europe it usually takes place in the form of industry–academia consortia, most often as government-funded research.

  13. 13.

    On collaborative translation, see O’Brien (2011).

  14. 14.

    See Pym (2011) for a discussion about these terms.

  15. 15.

    Pym (2011: 77) argues against the use of this term. Others prefer Translation Environment Tool (TEnT), a term coined by Jost Zetzsche.

  16. 16.

    For a comprehensive, albeit somehow outdated, categorization of electronic translation tools, see Austermühl (2001: 8–17).

  17. 17.

    For a more detailed definition of TM databases; an explanation of how analyse, concordance and matching functions work; potential of productivity and quality gains, etc., see Webb (1998).

  18. 18.

    The last statement is especially true among translation professionals. Historically, translation memory systems are an offspring of machine translation developments in the 1980s.

  19. 19.

    For an introduction to machine translation and its history, see Hutchins and Somers (1992). For a more recent overview of MT developments, see Way (2009).

  20. 20.

    Webb (1998) presents a comprehensive study of TM systems and summarises the state of affairs at the end of the 1990s, with a prophetic foresight for the following decade. For a very recent history and overview, with future prospects, see Zetzsche (2012).

  21. 21.

    See also Guerberof (2012) for a more recent version of this study.

  22. 22.

    Quite a few studies now suggest that with TM/MT translators adopt a “revise-as-you-go” approach (see Yamada 2011b; Martín-Mor 2011).

  23. 23.

    This brings about some interesting topics for discussion: Would there be a real difference between editing TM suggestions and MT suggestions in such an environment? To what extent would it make sense to differentiate between human-assisted machine translation (HAMT) and machine-assisted human translation (MAHT)?

  24. 24.

    Here I borrow the title of Anthony Pym’s (2004) book.

  25. 25.

    For a further discussion on this topic, see Pym (2012a, b).

  26. 26.

    For example, Wallis (2006) compares job satisfaction in two different translation modes (pre-translation vs. interactive). Her study involves only TM, but a similar comparison could be done to include MT.

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Acknowledgments

I would like to thank Anthony Pym, Amy Neustein, David Orrego-Carmona and Esmaeil Haddadian Moghaddam for revising earlier versions of this manuscript. I would also like to acknowledge the funding to my doctoral research, provided through the European Commission’s TIME Marie Curie fellowship (FP7-PEOPLE-2010-ITN-263954).

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  1. Intercultural Studies Group, Universitat Rovira i Virgili, Avda. Catalunya 35, Tarragona, 43002, Spain

    Carlos S. C. Teixeira

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Correspondence to Carlos S. C. Teixeira .

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    Judith A. Markowitz

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Teixeira, C.S.C. (2013). Multilingual Systems, Translation Technology and Their Impact on the Translator’s Profession. In: Neustein, A., Markowitz, J. (eds) Where Humans Meet Machines. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6934-6_14

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