Asynchronous updates to One-vs-All models create dataset divergences whose effect on system accuracy is captured by a new metric showing strong empirical correlation in language understanding tasks.
In this work, we explore one specific property of OV A systems, where each OV A model can be updated asynchronous of other OV A models
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One-vs-All Models for Asynchronous Training: An Empirical Analysis
Asynchronous updates to One-vs-All models create dataset divergences whose effect on system accuracy is captured by a new metric showing strong empirical correlation in language understanding tasks.