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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/272758045 Translation Skill-Sets in a Machine-Translation Age Article in Meta · January 2013 DOI: 10.7202/1025047ar CITATIONS 136 READS 2,302 1 author: Some of the authors of this publication are also working on these related projects: Mobility and Inclusion in a Multilingual Europe (MIME) View project Risk Management in Translation View project Anthony Pym Universitat Rovira i Virgili 194 PUBLICATIONS 3,973 CITATIONS SEE PROFILE All content following this page was uploaded by Anthony Pym on 27 November 2015. The user has requested enhancement of the downloaded file. Érudit est un consortium interuniversitaire sans but lucratif composé de l'Université de Montréal, l'Université Laval et l'Université du Québec à Montréal. Il a pour mission la promotion et la valorisation de la recherche. Érudit offre des services d'édition numérique de documents scientifiques depuis 1998. Pour communiquer avec les responsables d'Érudit : info@erudit.org Article "Translation Skill-Sets in a Machine-Translation Age" Anthony Pym Meta : journal des traducteurs / Meta: Translators' Journal, vol. 58, n° 3, 2013, p. 487-503. Pour citer cet article, utiliser l'information suivante : URI: http://id.erudit.org/iderudit/1025047ar DOI: 10.7202/1025047ar Note : les règles d'écriture des références bibliographiques peuvent varier selon les différents domaines du savoir. Ce document est protégé par la loi sur le droit d'auteur. L'utilisation des services d'Érudit (y compris la reproduction) est assujettie à sa politique d'utilisation que vous pouvez consulter à l'URI https://apropos.erudit.org/fr/usagers/politique-dutilisation/ Document téléchargé le 27 novembre 2015 10:26 Meta LVIII, 3, 2013 Translation Skill-Sets in a Machine-Translation Age anthony pym Universitat Rovira i Virgili, Tarragona, Spain Stellenbosch University, Stellenbosch, South Africa anthony.pym@urv.cat RÉSUMÉ L’intégration de la traduction automatique statistique (TA) aux logiciels de mémoire de traduction (MT) est en train de produire une gamme de technologies de MT/TA qui devraient remplacer dans de nombreux domaines la traduction entièrement humaine. Ce processus ouvre la voie à son tour à une transformation des compétences procédu- rales des traducteurs. Dans la mesure où les experts non traducteurs peuvent prendre en charge certaines tâches dans certains domaines, on s’attend à ce que les traducteurs s’occupent de plus en plus de la post-édition, sans avoir besoin de connaissances appro- fondies sur le contenu des textes, et éventuellement avec une insistance moindre sur la compétence dans la langue étrangère. Cette reconfiguration de l’espace traductif l’ouvre aussi aux fonctions productives des bases de données MT/TA, en sorte que l’on ne reconnaît plus l’organisation binaire autour du couple « source » et « cible » : nous avons affaire maintenant à un « texte de départ » accompagné de matériaux également de départ comme le sont les mémoires de traduction autorisées, les glossaires, les bases terminologiques et les propositions qui proviennent de la traduction automatique. Afin d’identifier les savoir-faire nécessaires pour travailler dans cet espace, on a recours ici à une approche « négative » et minimaliste : il faut tout d’abord identifier les problèmes de prise de décision qui résultent de l’emploi de des technologies MT/TA, pour ensuite essayer de décrire les compétences procédurales correspondantes. Nous proposons dix compétences de ce genre, organisées en trois groupes assez traditionnels : apprendre à apprendre, apprendre à accorder une confiance relative et raisonnée aux sources d’infor- mation, et apprendre à adapter la révision et la correction aux nécessités de la technolo- gie. L’acquisition de ces compétences peut être favorisée par une pédagogie qui intègre les espaces adéquats pour le cours de traduction, l’emploi transversal des technologies MT/TA, l’autoanalyse des processus traductifs, ainsi que les projets collaboratifs qui font appel aux experts non traducteurs. ABSTRACT The integration of data from statistical machine translation into translation memory suites (giving a range of TM/MT technologies) can be expected to replace fully human translation in many spheres of activity. This should bring about changes in the skill sets required of translators. With increased processing done by area experts who are not trained translators, the translator’s function can be expected to shift to linguistic poste- diting, without requirements for extensive area knowledge and possibly with a reduced emphasis on foreign-language expertise. This reconfiguration of the translation space must also recognize the active input roles of TM/MT databases, such that there is no longer a binary organization around a “source” and a “target”: we now have a “start text” (ST) complemented by source materials that take the shape of authorized translation memories, glossaries, terminology bases, and machine-translation feeds. In order to identify the skills required for translation work in such a space, a minimalist and “nega- tive” approach may be adopted: first locate the most important decision-making problems resulting from the use of TM/MT, and then identify the corresponding skills to be learned. 488 Meta, LVIII, 3, 2013 A total of ten such skills can be identified, arranged under three heads: learning to learn, learning to trust and mistrust data, and learning to revise with enhanced attention to detail. The acquisition of these skills can be favored by a pedagogy with specific desid- erata for the design of suitable classroom spaces, the transversal use of TM/MT, students’ self-analyses of translation processes, and collaborative projects with area experts. MOTS-CLÉS/KEYWORDS savoir-faire du traducteur, compétence traductive, formation des traducteurs, technolo- gies de la traduction, post-édition translation skills, translation competence, translator education, translation technology, postediting 1. Introduction My students are complaining, still. They have given up trying to wheedle their way out of translation memories (TM); most have at last found that all the messing around with incompatibilities is indeed worth the candle: all my students have to translate with a TM all the time, and I don’t care which one they use. Now they are complain- ing about something else: machine translation (MT), which is generally being inte- grated into translation memory suites as an added source of proposed matches, is giving us various forms of TM/MT. These range from the standard translation- memory tools that integrate machine-translation feeds, through to machine transla- tion programs that integrate a translation memory tool. When all the blank target-text segments are automatically filled with suggested matches from memories or machines, that’s when a few voices are raised: “I’m here to translate,” some say, “I’m not a posteditor!” “Ah!,” I glibly retort. “Then turn off the automatic-fill option…” Which they can indeed do. And then often decide not to, out of curiosity to see what the machine can offer, if nothing else. The answer is glib because, I would argue, statistical-based MT, along with its many hybrids, is destined to turn most translators into posteditors one day, perhaps soon. And as that happens, as it is happening now, we will have to rethink, yet again, the basic configuration of our training programs. That is, we will have to revise our models of what some call translation competence.1 2. Reasons for the revolution MT systems are getting better because they are making use of statistical matches, in addition to linguistic algorithms developed by traditional MT methods. Without going into the technical details, the most important features of the resulting systems are the following: 1. The more you use them (well), the better they get. This would be the “learning” dimension of TM/MT. 2. The more they are online (“in the cloud” or on data bases external to the user), the more they become accessible to a wide range of public users, and the more they will be used. translation skill-sets in a machine-translation age 489 These two features are clearly related in that the greater the accessibility, the greater the potential use, and the greater the likelihood the system will perform well. In short, these features should create a virtuous circle. This could constitute something like a revolution, not just in the translation technologies themselves but also in the social use and function of translation. Recent research indicates that, for Chinese- English translation and other language pairs,2 statistical MT is now at a level where beginners and Masters-level students with minimal technological training can use it to attain productivity and quality that is comparable with fully human translation, and any gains should then increase with repeated use (Pym 2009; García 2010; Lee and Liao 2011). In more professional situations, the productivity gains resulting from TM/MT are relatively easy to demonstrate.3 Of course, as in all good revolutions, the logic is not quite as automatic as expected. When free MT becomes ubiquitous, as could be the case of Google Translate, uninformed users publish unedited electronic translations with it, thus recycling errors that are fed back into the very databases on which the statistics oper- ate. That is, the potentially virtuous circle becomes a vicious one, and the whole show comes tumbling down. One solution to this is to restrict the applications to which an MT feed is available (as Google did with Google Translate in December 2011, making its Application Program Interface a pay-service, and as most companies should do, by developing their own in-house MT systems and databases). A more general solution could be to provide short-term training in how to use MT, which should be of use to everyone. Either way, uploads/Industriel/ translation-skill-sets-in-a-machine-translation-age.pdf