It was unrealistic as a solution to aid natural language understanding at scale and comprehensive ranking in web search. , and really only used on Mexico Phone Number List most nuanced queries with multiple meanings in phrases and sentences, and certainly not on any scale. But it's not all bad news for bert throughout 2019 and 2020, there have been great strides aimed at making bert-like technologies much more useful than an impressive “good Mexico Phone Number List to have”. The question of the content of long documents has already been addressed big bird, longformer and clusterformer since the majority of performance issues seem to be about this quadratic dependence.

In transformers and its impact on performance and expense, more recent work aims to turn this quadratic dependence into linear, with the most prominent of these being longformer: the long document transformer (beltagy, 2020) and google's big bird (zaheer et al, 2020) . Big bird's abstract for the paper reads: “the proposed sparse attention can Mexico Phone Number List handle sequences of length up to 8x of what was previously possible using similar hardware. Due to its ability to handle longer context, bigbird significantly improves performance on various nlp tasks such as question answering and summarizing. » not to be outdone, in mid-october microsoft researchers (wang et al, 2020) presented their paper on cluster-former.

The cluster-former model is sota on google's "Long answer" ranking of natural questions. Both of these models also seek to address the limitations of Mexico Phone Number List long-form content. And now the 'performers' are rethinking the transformers also very recently (october 2020), a combined work between google, cambridge, deepmind and alan turing institute was published to address the efficiency and scale issues of the transformer architecture as a Mexico Phone Number List whole in an article titled “rethinking attention with performers” (choromanski et al., 2020) , offering a comprehensive revisit of the fundamental operation of the attention mechanism, intended