AirBnB has previously claimed that its Translation Engine has improved over 99% of its listings—a bold claim if ever there was one—and continues to improve and do better over time.
If you’ve been keeping up with our blog, you probably already know how that works. Machine translation today uses state of the art machine learning models that learn from large quantities of language data. AirBnB definitely has access to that kind of data, claiming to process over 3.5 million messages daily and having over 500 million reviews on its listings to date.
Another notable thing about this data they have on hand is that it’s very domain-specific. What this means is that the same kinds of language and terminology of a given sector—in this case, the hospitality oman mobile database industry—tend to pop up and be translated more often. This makes the machine translation system much better at translating texts within that sector compared to generic models like, say, Google Translate.
AirBnB’s data has the advantage of being hyperspecific, which means that the quality of machine translation for its specific purpose would also tend to be higher. You wouldn’t use AirBnB’s Translation Engine for, say, technical documents, but it’s perfectly tailored to translate rental listings.
Providing a seamless experience for all users
And that’s just the back end of things. On the front end, Translation Engine has provided quality of life upgrades not just for the app’s guests, but also for its hosts.
Before AirBnB’s Translation Engine, hosts would have to create listings in different languages manually. They would translate text in their language to another, often using a generic machine translation system like Google Translate, then do it again for a different language.