At the METM16 conference, I saw a talk that caused me to ponder something I don’t think about very much: machine translation (MT). I had tried it on occasions but had always quickly cast it aside. It just didn’t seem to help. So every time I heard someone talking about the importance of MT (for good or bad), I ended up feeling guilty for not paying attention to something that will, and probably already is, affecting my livelihood. However, this talk sparked a revelation into why I care so little for it.
The talk was actually three talks: one by an MT expert, Diego Bartolome, and two by users of personal MT software, Alan Lounds and Emma Goldsmith. Predictably, the expert sung the praises of MT. He seemed mainly to be talking about using MT as part of a post-editing process in which the translator gets the MT output to edit. So he wasn’t quite coming to us with a translator’s perspective in mind. In any case, he made it clear that MT is improving and becoming more widely used by language service providers.
After Diego, Emma and then Alan explained their experiences with two different MT tools. Because they are both translators, what they had to say aligned with how translators would use machine translation. They talked about the ins and outs of using personal MT products that you either customise yourself or that adapt to how you work. From what they said, I could see how these tools might be useful.
However, I still had doubts about how much I would benefit from such a tool given that it didn’t seem much of a step up from what some CAT tools are already doing, as someone at the talk pointed out. And I’m not talking here about using an external MT engine through the CAT tool but the native CAT functions for assembling segments. In any case, both these technologies might converge in the long run. CAT tools will become freelance MT tools and vice versa.
During the questions at the end of the talk, while I was sitting there trying to reconcile the overwhelmingly positive view of MT coming out of the talk with my overwhelmingly negative experience with it, Tim Barton asked Diego if speed comparisons had been done between translating with voice recognition software versus MT. As far as Diego was aware, no such tests had been done. (Logical, if you think about, given that Diego’s focus seemed geared towards a post-editing model in which the translator doesn’t do the whole job.) And with this question I thought I had my answer to why I’d written off machine translation: it probably saved little or no time compared to translating with voice recognition software, which was also more or less the point of the person asking the question. Of course, you only get this time-saving if you’re doing the whole job yourself.
So, thinking I’d solve the dilemma as to why MT had proven useless to me, I let the matter rest there. However, a few days after the conference, I did a job that made me change my mind. It was a legal/marketing text on real estate that turned out to have quite a few pages full of construction terminology. It was very dense. And so it occurred to me that I could use machine translation to work my way through the thickets of building terms. I thought I’d turned to MT because those positive comments about MT still echoed in my brain, but later I realised something else probably made me think of using MT at that moment.
So I used Google Translate. And it did help me get some technical terms. But after post-editing a couple of paragraphs of the MT output, I started to translate from scratch and only use Google’s attempts as a guide. Then I only used it occasionally as a dictionary for some terms. In the end, I found translating from scratch quicker than fixing up the MT output, even for sentences clogged with technical jargon.
With practice and using a better tool, I imagine I could streamline the process, but the main point is that I later realised why I don’t use machine translation. I surely had turned to it on this occasion because I was being stared down by menacing patches of text outside my fields of specialisation. I rarely translate subjects I don’t know something about, and I realised I’d never consider using MT for any field I’m comfortable with and definitely wouldn’t use it for those I know back to front. I just don’t think it would be quicker — even if I didn’t use voice recognition software. So my conclusion is that specialisation trumps MT.
I should say that often in the fields I know well, I do come across areas I’m not familiar with. In these cases, I sometimes have to do a lot of research, but that doesn’t bother me as I see it time well spent on getting to know my areas of specialisation better. So even in this case, I doubt I’d be tempted by even a near-perfect MT tool.
This is, of course, all from the translator’s perspective — when you’re translating the text from start to finish. An outsourcer can obviously benefit from MT because, following the post-editing model, they do the machine translation, and the translator just does the revising. So, after the initial investment and cost to use the tool, an outsourcer saves by paying less to the translator.
So now I know where my lack of interest in machine translation comes from. I’m sure my apathy will come back to bite me someday, but at the moment it seems warranted, at least for my situation. As I said at the beginning, it is possible MT has already affected my livelihood in some way. But, as one-man show that doesn’t rely on a high volume of jobs, it’s impossible for me to see any real changes.