I’ve been working in the language services industry for a while now, so I used to think that I was already familiar with all of the TMS systems there were. I was right about this for a while. However, these days it seems there is a new language technology provider (LTP) popping up every week. This is great news for our industry as we are long overdue for some meaningful innovation in this area. However, it can be a struggle to stay up to speed with all the latest technologies out there. Sometimes I can’t help but feel like an old man in my rocking chair shaking my head at all the newfangled technologies. Do I detect some laughter? I’m quite sure some of you can relate!
Every time I open up Facebook (And let’s not even discuss the transition to Snapchat…), I see an advertisement for a particular translation company that I have never heard of before. (I’m not going to say the name because it is not relevant here). And when I say “every time”, I’m really not exaggerating. This ad campaign is aggressive. The ad has even followed me to other sites, showing up in banners and popups while I am just minding my own business online. It is quite literally following me. It won’t stop. All I want to do is watch the latest Star Wars trailer in peace, but this stupid ad won’t leave me alone.
So I finally gave into the pressure (yes, I usually end up doing whatever Facebook tells me to do) and clicked on the advertisement. I wanted to see what this was all about. The website is slick. It goes into detail about how their innovative new platform includes such features as “built in spellchecker” and something called “translation memories.” I’ll be perfectly honest, if I were somebody else (somebody with less experience in this industry), this product would sound pretty damn fabulous.
However, I’m not somebody else. I’m me. And it took me about 10 minutes to realize that what I was looking at wasn’t even a new translation company. The advertisement was actually for a machine translation platform. Ah, OK! That’s fine. Machine translation is great! However… What made me suspicious was how long it took me to realize that there was no actual human translation involved. From all of the content on the website, any inexperienced (yet reasonably intelligent) consumer could easily make the assumption that they would be buying human performed translations. Indeed, inexperienced consumers generally don’t know the difference between machine translation and human translation – until, that is, their foreign customers start screaming at them.
Delving a little deeper, I started looking at the various features that this platform had to offer. Here are a few that I jotted down:
Editor’s note: They are referring to custom-training the MT engines. However, there is no mention that this will of course mean hiring linguists to post-edit the content. The website made it seem like this was a breeze. However, we know that in order to train a machine translation engine, we have to have human translators involved in this process. Nine times out of ten, this means hiring an LSP. Any self-respecting LSP is going to already have their preferred methodology for training and maintaining MT engines that allows them to be more efficient, but now you will be forcing them to work in this tool that they have never heard of before. Good luck with that.
Editor’s note: Really? Translation Memories? Innovative? Is this 1993? I’m not even going to comment on this further… I just can’t…
The language services industry is undergoing a profound transformation with the emergence of cutting-edge technologies such as ChatGPT and large language models (LLMs). These powerful language generation models have captivated the attention of businesses and language professionals alike, offering exciting possibilities for translation, localization, and content creation.
It’s already been six years now since Google revealed that Google Translate processes 146 billion words a day — three times more than what all the professional translators in the world combined can do in a month. That was 2016 and things haven’t really slowed down in the machine translation (MT) universe since.
We recently introduced you to the two- (or five-) second rule, which is essentially the reaction or decision-making time a linguist should spend judging whether to post-edit a segment of machine translation (MT) output or to retranslate it. This rule of thumb aims to help increase the linguist’s productivity when working with MT.
If you’re a driver, you’ve probably heard of the two-second rule. Staying at least two seconds behind any vehicle is considered a rule of thumb for drivers wanting to maintain a safe following distance at any speed. The two seconds don’t represent safe stopping distance but rather safe reaction time.