The Linguist is a languages magazine for professional linguists, translators, interpreters, language professionals, language teachers, trainers, students and academics with articles on translation, interpreting, business, government, technology
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thelinguist.uberflip.com DECEMBER 2016/JANUARY 2017 The Linguist 13 FEATURES machine translation. In some cases, at least, it has resulted in a division of labour between human and machine that assigns the most mechanical of tasks to the human. It is a classic case of deskilling, in which a complex activity previously accomplished, start to finish, by one person is broken up into a series of simplified tasks, requiring less skill than before from the humans involved. No wonder many translators are less than enthusiastic. A further cause of unease must surely lie in the instruction frequently given to post- editors that they should make the MT output just 'good enough'. Understandably, many translators/post-editors struggle with a brief that explicitly requires them not to do the job to the best of their ability. But that, of course, is not the full story. Let us return to the prediction that machine translation will soon turn most translators into post-editors. We have already seen economic reasons why things might not work out this way, but there are other reasons too. One is that it is perfectly possible for translators to take ownership of the entire translation workflow, rather than being restricted to a single task at the end, even if that workflow has been largely automated. Recall that SMT and NMT both rely on bilingual training data; translators who have been using TM tools for years are likely to have at their disposal training data that are particularly appropriate for the kind of work they take on. For the last decade, open-source tools have existed that allow such translators to use these data to build their own SMT engines. Toolkits such as Moses were initially difficult to use, but over the last five or so years, cloud-based and other SMT services have emerged that make self-service SMT much more accessible. Such services are now covered in at least some university translator training programmes. 10 We have yet to see whether do-it-yourself neural machine translation will become a reality, but given the right hardware, a suitable response from software engineers and appropriate education for translators, there is no reason to discount this possibility. No matter how NMT pans out, at the very least translators and interpreters should attend closely to issues of data ownership and how they wish their digital activity to be logged. Even if the actions of technological elites over the last decade have served to normalise the large-scale online harvesting of all kinds of data, there are legal, economic and political reasons why such harvesting is no longer going unquestioned. Technology watchers are questioning the sustainability of our current 'winner-takes-all' digital economy, and national governments and the European Union, in particular, are beginning to react to some of the more pernicious effects of big-data-enabled digital disruption. Translators and interpreters would do well to heed such developments. They may yet turn out to be just as important to their careers as any technical advances we see in translation technology over the coming years. The Threlford Memorial Lecture 2016 was given on 17 September at Stationers' Hall. Notes 1 Cronin, M (2013) Translation in the Digital Age, London: Routledge, 3 2 See Pym, A (2013) 'Translation Skill-sets in a Machine-Translation Age'. In Meta 58(3): 487-503 3 See, e.g, www.eu-bridge.eu/lecture.html 4 See www.waverlylabs.com 5 Koskinen, K and Ruokonen, M (2017) 'Love Letters or Hate Mail? Translators' technology acceptance in the light of their emotional narratives'. In Kenny, D (ed) Human Issues in Translation Technology, London: Routledge 6 See https://research.googleblog.com/2016/ 09/a-neural-network-for-machine.html 7 See Frey, C B and Osborne, M A (2013) 'The Future of Employment: How susceptible are jobs to computerisation?'; www.oxfordmartin.ox.ac.uk /downloads/academic/The_Future_of_ Employment.pdf 8 Koehn, P, et al (2013) Casmacat Final Public Report, Edinburgh: University of Edinburgh; www.casmacat.eu 9 Moorkens, J and O'Brien, S (2017) 'Assessing User Interface Needs of Post-Editors of Machine Translation'. In op cit. Kenny, D 10 See, e.g, Doherty, S and Kenny, D (2014) 'The Design and Evaluation of a Statistical Machine Translation Syllabus for Translation Students'. In The Interpreter and Translator Trainer, 8(2): 295-315a Urls accessed 20/10/16 It doesn't look like interpreters and translators should be throwing in the towel any time soon ANIMATED Dorothy's engaging talk