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Nayak says MUM was trained on a high-quality subset of the web corpus after omitting low-quality content. Like BERT it can understand language using encoders… but in addition to that it also has a stack of decoders which allows it to generate text,” Nayak says, adding that this particular architecture is a text-to-text model based on T5 architecture developed by Google Research. “But in many ways MUM is very different from BERT.
In 2018, Google launched BERT, a neural network-based technique for natural language processing (NLP) pre-training that lets anyone train their own state-of-the-art question answering system. “MUM is a deep neural network that’s made up of transformers just like BERT (Bidirectional Encoder Representations from Transformers) was,” explains Nayak on a call. “After validating MUM’s findings, we applied them to Google Search so that people could find timely, high-quality information about COVID-19 vaccines worldwide.” So now the search platform uses MUM “to identify over 800 variations of vaccine names in more than 50 languages in a matter of seconds,” Pandu Nayak, Google Fellow and Vice President, Search wrote in a blog post. Since AstraZeneca, CoronaVac, Moderna, Pfizer, Sputnik and other broadly distributed vaccines all have many different names all over the world, Google’s “ability to correctly identify all these names is critical to bringing people the latest trustworthy information about the vaccine”. So when it faced a similar challenge around Covid-19 vaccine names, the tech giant turned to its new Multitask Unified Model (MUM) tool for help. The same thing having similar names in different parts of the world or cultures, is a problem Google search has encountered for long.
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