Imagine this: you’re waiting in line at the DMV, watching paint dry, listening to the soothing symphony of an old dot-matrix printer, and thinking, “Surely, nothing in life could be slower than this.” Well, congratulations, because until recently, Cattitude’s term-matching algorithm could give the DMV a run for its money—if (and this is a big if) you threw the most absurdly complex project imaginable at it.
Here’s the thing. In normal day-to-day usage, no one—not a single one of our users—ever noticed a problem. Not one. In fact, the only complaints we got about Cattitude were along the lines of: “It’s already so fast, I can’t even keep up with it.” Which, yes, is the translation industry equivalent of someone complaining that their private jet gets them to Monaco too quickly. But we digress.
What we’re talking about here was only ever an issue when working with absolutely monstrous termbases containing 10,000 terms or more, combined with projects that featured ludicrously long terms (we’re talking 15 words!) and equally ludicrously long sentences (oh, you know, just your casual 100 words or so). And these aren’t your run-of-the-mill, everyday translation projects. No, no. These are the kinds of Frankenstein test cases we throw at Cattitude internally—because if we’re going to break something, we’d rather it happen in our lab than on your screen.
Still, when you’re running tests like this, flaws emerge. And in this particular scenario, things got… slow. The culprit? Our old approach was the equivalent of a hyperactive librarian, meticulously checking every possible combination of words in the source sentence against the termbase. Combine that with an overly ambitious default maximum term length, and voilà—an algorithm that spent way too much time searching for phrases like “How many woodchucks would a woodchuck chuck if a woodchuck could chuck wood?” even when those terms weren’t remotely relevant.
But don’t worry—we’ve fixed it. And when we say fixed it, we mean we handed that hyperactive librarian a shot of espresso and taught them how to work smarter, not harder. Now, we’ve introduced logic that limits term candidate lengths to one word by default, only expanding that limit if the termbase actually contains longer terms and those terms are likely to appear in the source. The result? The unnecessary busywork is gone, and Cattitude is leaner, meaner, and faster than ever.
Just how much faster, you ask? Well, in one of those aforementioned monster test cases, the total matching time dropped from 51,340 milliseconds to a mere 3,731 milliseconds. That’s over 13 times faster, all while maintaining 100% accuracy. And remember, these are internal test cases—normal users weren’t even experiencing a problem. But now? Oh, now they get to “enjoy” a tool so fast it makes the old version look sluggish by comparison. If you thought you couldn’t keep up with Cattitude before, you’re in for a wild ride now.
So yes, irony alert: the only thing anyone’s going to complain about now is that Cattitude’s insane speed has become an even bigger “problem.” But hey, isn’t that a nice problem to have? The DMV, on the other hand? Sorry, still can’t help you there.