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.
We use the SLU model setup devel- oped by Su et al
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.