Coursework: Technology in Translation

The second module of my translation masters was about technology in translation. The industry often advances hand in hand with technology, with the usual disparity between people who have “an idea of translation” and make the tech, and the people who “do translation” and use the tech. Like many things, its easy to press the button—doesn’t mean you know what you’re doing.

Thus one of the jobs of the professional is exactly that—know what they’re doing with the tools available. This module covered a wide array of applications, notably CAT (Computer Assisted Translation) tools which, for the uninitiated, are a kind of multi-tool/office suite for translation tasks with a strong focus on utilising and managing Translation Memories and Term Bases, as well as a load of other functions including Desk-Top Formatting (DTP), integrating dictionaries and thesauruses, pre-programming rules based on your style guide, so on and so forth.

In the end, I chose to focus on Trados Studio as it worked primarily offline, which has a number of benefits, most notably portability and security, although I also experimented with Phrase (once Memsource) to get a feel for different kinds of tools. My course also offered free access to Trados training courses and certifications, and so I’ve passed the Level 1 certifications for Studio and MultiTerm (the later levels focus more on the project management rather than translation side of the tools, although I’ve given the materials a once over to pick out anything that would be immediately useful).

Of course, we covered Machine Translation as well, both its benefits and limitations. This was shortly before the AI boom really…“boomed”, so I missed out a little on that discussion, but at least when it comes to translation, the same issues that apply to, say, Neural Machine Translation (which works very similar to AI and where AI innovation had already been happening years before the hype) also apply to AI translation. So typically, when I refer to Machine Translation, I am including AI models to that.

Although I will write later on Machine Translation, my general stance is that it is part of the job, has its place in certain environments where you have a large amount of controlled text, and that the only people qualified to judge MT output of any kind are translators because they know what they’re looking for. Indeed, the people you want using those engines are translators because they know what they’re looking for from start to finish.

This module, too, put great emphasis on teaching us Machine Translation Post Editing (MTPE) best practices so that we could assess and adapt to the quirks of any engine—and they all have quirks. Knowing these can make utilising the tool or post-editing the output far more efficient and accurate. Indeed, the purpose of the coursework this time around was to test our proficiency with CAT tools and at assessing MT output both.