How BERT Determines Search Relevance

In fact, when it comes to ranking results, BERT will help Search better understand one in 10 searches in the U.S. in English, and we'll bring this to more languages and locales over time. Google's remarks and explanations raise some key questions: In 2015, Crowdflower (now Appen Figure-Eight Crowdflower) hosted a Kaggle competition [2] where data scientists built models to predict the relevance for search results given a query, a product name and a product description. The winner, ChenglongChen pocketed $10,000 when his best model took first place by scoring 72.189% [3]. Although the competition has been closed for five years, the data set is still available and the Kaggle competition scoring functionality still works for the private leaderboard (it just doesn't award any site points). I pulled the data, fine tuned a BERT classification model, predicted a submission, and it scored 77.327% [4].