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Lilt is building a machine translation business with humans at the core

The ability to quickly and automatically translate anything you see using a web service is a powerful one, yet few expect much from it other than a tolerable version of a foreign article, menu, or street sign. Shouldn’t this amazing tool be put to better use? It can be, and a company called Lilt is quietly doing so — but crucially, it isn’t even trying to leave the human element behind.
By combining the expertise of human translators with the speed and versatility of automated ones, you get the best of both worlds — and potentially a major business opportunity.
The problem with machine translation, when you really get down to it, is that it’s bad. Sure, it won’t mistake “tomato” for “potato,” but it can’t be trusted to do anything beyond accurately translate the literal meaning of a series of words. In many cases that’s all you need — for instance, on a menu — but for a huge amount of content it simply isn’t good enough.
This is much more than a convenience problem; for many language provides serious professional and personal barriers.
“Information on a huge number of topics is only available in English,” said Lilt co-founder and CEO Spence Green; he encountered this while doing graduate work in the Middle East, simultaneously learning Arabic and the limitations placed on those who didn’t speak English.
Much of this information is not amenable to machine translation, he explained. Imagine if you were expected to operate heavy machinery using instructions run through Google Translate, or perform work in a country where immigration law is not available in your language.
“Books, legal information, voting materials… when quality is required, you need a human in the loop,” he said.
Working on translation projects there and later at Google, where he interned in 2011, Green found himself concerned with how machine translation could improve access to information without degrading it — as most of the systems do.
His realization, which he pursued with co-founder John DeNero, was that machine learning systems worked well not simply as a tool for translation, but as tool for translators. Working in concert with a translation system makes them faster and better at their work, lightening the cognitive load.
The basic idea of Lilt’s tool is that the system provides translations for the next sentence or paragraph, as a reference for structure, tense, idiom, and so on that the translator can consult and, at least potentially, work faster and better. Lilt claims a 5x increase in words per hour translated, and says the results are as good or better than a strictly human translation.
Lilt is building a machine translation business with humans at the core
“We published papers — we knew the technology worked. We’d worked with translators and had done some large-scale experiments,” Green said, but the question was how to proceed.
Talk to a big company and get them interested? “We went through this process of realizing that the big companies are really focused on the consumer applications — not anywhere there’s a quality threshold, which is really the entire translation industry,” Green said.
Stay in academic research, get a grant and open-source it? “The money kind of dried up,” Green explained: money was lavishly allocated after 9/11 with the idea of improving intelligence and communication, but a decade later the sense of urgency had departed, and with it much of the grant cash.
Start a company? “We knew the technology was inevitable,” he said. “The question was who would bring it to market.” So they decided it would be them.
Interestingly, a major change in language translation took place around the time they were really getting to work on it. Statistical neural network systems gave way to attention-based ones; these have a natural sort of affinity to efficiently and effectively parsing things like sentences, where each word exists not like a pixel in an image, but is dependent on the words nearby it in a structured way. They basically had to reinvent their core translation system, but it was ultimately for the better.

Google’s Transformer solves a tricky problem in machine translation
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