Abstracts
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 postediting, 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 “negative” 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. 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 desiderata 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.
Keywords:
- translation skills,
- translation competence,
- translator education,
- translation technology,
- postediting
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 procédurales 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 approfondies 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’information, et apprendre à adapter la révision et la correction aux nécessités de la technologie. 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.
Mots-clés :
- savoir-faire du traducteur,
- compétence traductive,
- formation des traducteurs,
- technologies de la traduction,
- post-édition
Appendices
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