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How Facebooks new way of classifying what you write may speed feature rollouts across the globe

Posted on: January 26, 2018, in Blog

The way Facebook processes what “the worlds” writes is about to get a bit more cosmopolitan.

As Facebook’s scope continues to grow globally, the style it rolls out features has been complicated by the fact that there are more than 100 languages currently supported on the site. When it comes to building text containers that users can form status updates into, this isn’t that difficult of a number of problems, but as artificial intelligence continues to drive everything Facebook does, the challenges skyrocket for ensuring that its systems amply grasps what its customers are wanting.

The company’s Applied Machine Learning team has expended the past year working on a engineering called multilingual embeddings which it says could significantly improve the speed at which its natural language processing tech is able to operate across foreign speeches. In early tests, the new process is 20-30X faster than previous methods, the company said.

Beyond reductions in latency, the tech could help future Facebook features reach more people more quickly and ensure much more consistency across what services the website gives across the globe

“From the multilingual insight view, I want everybody to use all the features that are deployed by Facebook in their own speech, ” Facebook head of translation Necip Fazil Ayan told TechCrunch in an interview. “This should not be limited to a specific language, but we want to move to a world where all features are available everywhere, and can be used by everybody.”

The company has already been utilizing the tech over the past several months to detect content-policy misdemeanors, surface M Suggestions in Messenger and power its Recommendations feature across several languages. Facebook has about 20 technologists inside its AML group working on its own language and translation technologies.

Word embeds are basically vectors that allow text classifiers to approach human language in a more context-driven way, highlighting the interrelatedness of words to eventually deduce shared meaning or intent.( Here‘s a good breakdown if you’re curious .) Corporations like Facebook can construct( and have built) term embeddings for individual languages, but it’s pretty labor intensive to gathering the training data for classifiers when you’re dealing with more than 100 languages FB supports, thus they’ve had to work towards a more scalable approach.

Simplified sample word embeds highlighting separate word vectors in Spanish and English for “soccer”

Previously it’s led to the company basically translating foreign speeches to English and then operating English classifiers on them, but this has been a rough answer due to translation corrects, but perhaps more importantly the solution has been far too slow. By mapping multiple languages onto similar term vectors, a blog post from the company details, Facebook’s method “can train on one or more languages, and learn a classifier that works on languages “youve never” read in training.”

Even with the 20 -3 0 significant reduction in latency, Facebook says that this approach is reading makes similar to what it would be getting with language-specific classifiers in some early testing.

The company’s work is still in its early stages when it comes to language support, right now feature rollouts utilizing the tech support French, German and Portuguese though Ayan says that internally the team has been investing in tech that works in the “tens of languages.” Furthermore, the group is working to improve accuracy by to be built sentence and paragraph embeddings that get to the root intent of a body of text even more quickly.

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