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In this paper we argue that the time is ripe for translator educators to engage with Statistical Machine Translation (SMT) in more profound ways than they have done to date. We explain the basic principles of SMT and reflect on the role of humans in SMT workflows. Against a background of diverging opinions on the latter, we argue for a holistic approach to the integration of SMT into translator training programmes, one that empowers rather than marginalises translators. We discuss potential barriers to the use of SMT by translators generally and in translator training in particular, and propose some solutions to problems thus identified. More specifically, cloud-based services are proposed as a means of overcoming some of the technical and ethical challenges posed by more advanced uses of SMT in the classroom. Ultimately the paper aims to pave the way for the design and implementation of a new translator-oriented SMT syllabus at our own University and elsewhere.

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This paper focuses on some of the methodological and theoretical challenges presented by the investigation of "sanitisation" in translated texts through the analysis of semantic pros- ody. The main hypothesis is that target texts tend to use toned down vocabulary compared with their sources, and that this results in the creation of a "sanitised version of the original."Cet article se concentre sur certains des défis méthodologiques et théoriques de l'étude de l'"assainissement" des textes traduits par l'analyse de la prosodie sémantique. L'hypothèse principale est que le vocabulaire des textes cibles est généralement plus restreint que celui de l'original; il en résulte ce que nous appelons des "versions assainies de l'original"

A Reception Study of Machine Translated Subtitles for MOOCs Ke HuAs MOOCs (Massive Open Online Courses) grow rapidly around the world, the language barrier is becoming a serious issue. Removing this obstacle by creating translated subtitles is an indispensable part ofdeveloping MOOCs and improving accessibility.Given the large quantity of MOOCs availableworldwide and the considerable demand for them, machine translation (MT) appearsto offer an alternative or complementary translation solution, thus providingthe motivation for this research.

Many universities have now incorporated commercially available translators ' workbench-style systems into their translator-training programmes. But, when it comes to computer-aided translation (CAT), the university's role need not be confined to teaching students how to operate some third party's system; rather new CAT tools open up whole new areas of research. For example, experience of Trados's Translator's Workbench suggests that workbench features such as automatic terminology recognition and translation memories stand to bring about fundamental changes in the way terminology is recorded and texts are authored. State-of-the-art CAT tools can also make a contribution to Descriptive Translation Studies and translation pedagogy. Résumé: Les postes de travail du traducteur (PTT) font désormais partie intégrante de nombreux programmes universitaires de formation des traducteurs/ traductrices. Des logiciels tels que le Translator's Workbench de Trados ouvrent de nouvelles perspectives de recherche aux universitaires. Dans cet article, nous étudions les usages actuels et futurs des PTT ayant trait à la rédaction de textes, la terminographie, la pédagogie de la traduction et la traductologie.

Recent work in translation studies has established the literary translator’s voice as an ethical concern, but there has been little empirical research so far into how the translator’s voice is affected in workflows involving machine translation. In this article, we investigate how the use of neural machine translation influences the textual voice (Alvstad et al. 2017) of renowned translator from English into German, Hans-Christian Oeser. Based on an experiment in which Oeser post-edits an excerpt from a novel he had previously translated, we show how his textual voice is somewhat diminished in his post-edited work compared to its stronger manifestation in his translation work. At the same time Oeser’s contextual voice (ibid.) remains strong in his comments on the text he produces in post-editing mode. The article is offered as a methodological intervention and represents an initial attempt to design studies in literary machine translation that put the focus on human translators, allowing their voices to be heard more clearly than has previously been the case.

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