So where to go and why only 7% of queries? Thus, we know that bert was used, at least in part, for 10% of queries, and that was probably in the second stage of ranking (re-ranking) due to computational costs, and probably only on the most queries. More nuanced, and probably not as a re-ranking or pass-ranking, but as a sentence-level Mexico Phone Number List disambiguation and text summarization (featured snippets) task tool. We know Mexico Phone Number List that neural ranking approaches with bert and other deep neural networks have been computationally too expensive at the first stage of research in the search industry and that there have been limits on the number number of tokens that bert can work with - 512 tokens.
But 2020 has been a big year and developments to scale natural language machine learning attention systems have included innovations such as big bird, reformer, performers and electra plus t5 to test the limits of learnings. By transfer, which has made Mexico Phone Number List enormous progress. And these are just projects that google is involved in to some degree. Not to mention the other major technology research companies. While much of this work is very new, a year is a long time in the ai nlp research space, so expect huge changes by next year. Whether or not .
Deepct is used in the next production search passage indexing feature, it's very likely that bert has a strong connection to the change, given the overwhelming use of bert (and friends) as a reranker passing through research in the past 12 months. Mexico Phone Number List Passages, with their limited number of tokens, if taken as stand-alone pieces, can argue, by their nature, limit the effectiveness of keywords alone without contextual Mexico Phone Number List representation, and certainly, a word-stuffed passage- keys to overcoming it would rather be a step backwards. Than getting away from keyword search engines are trying to get away. By using contextual representations to understand the meaning of a word in a given context,