This breakthrough was the result of Google research on transformers: models that process words in relation to all the other words in a sentence, rather than one-by-one in order. This technology enables anyone to train their own state-of-the-art question answering system. With the latest advancements from our research team in the science of language understanding-made possible by machine learning-we’re making a significant improvement to how we understand queries, representing the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of Search.Īpplying BERT models to Search Last year, we introduced and open-sourced a neural network-based technique for natural language processing (NLP) pre-training called Bidirectional Encoder Representations from Transformers, or as we call it- BERT, for short. In fact, that’s one of the reasons why people often use “keyword-ese,” typing strings of words that they think we’ll understand, but aren’t actually how they’d naturally ask a question. While we’ve continued to improve our language understanding capabilities over the years, we sometimes still don’t quite get it right, particularly with complex or conversational queries. It’s our job to figure out what you’re searching for and surface helpful information from the web, no matter how you spell or combine the words in your query.
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We might not know the right words to use, or how to spell something, because often times, we come to Search looking to learn-we don’t necessarily have the knowledge to begin with.Īt its core, Search is about understanding language. When people like you or I come to Search, we aren’t always quite sure about the best way to formulate a query. We see billions of searches every day, and 15 percent of those queries are ones we haven’t seen before-so we’ve built ways to return results for queries we can’t anticipate. If there’s one thing I’ve learned over the 15 years working on Google Search, it’s that people’s curiosity is endless.
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